Internet resource discovery services

An overview of resource discovery services currently available on the Internet is presented. The authors concentrate on the following discovery tools: the Wide Area Information Servers (WAIS) project, Archie, Prospero, Gopher. The World-Wide Web (WWW), Netfind, the X.500 directory, Indie, the Knowbot Information Service (KIS), Alex, Semantic File Systems, and Nomenclator. The authors summarize the surveyed tools by presenting a taxonomy of their characteristics and design decisions. They also describe where to find and how to access several of the surveyed discovery services. They conclude with a discussion of future directions in the area of resource discovery and retrieval     

Direction-aware resource discovery in large-scale distributed computing environments

As a system scales up, the peer-to-peer (P2P) approach is attractive to distributed computing environments, such as Grids and Clouds, due to the amount of resources increased. The major issue in large-scale distributed systems is to prevent the phenomenon of a communication bottleneck or a single point of failure. Conventional approaches may not be able to apply directly to such environments due to restricted queries and varied resource characteristics. Alternatively, a fully decentralized resource discovery service based on an unstructured overlay, which relies only on the information of resource attributes and characteristics, may be a feasible solution. One major challenge of such service is to locate desired and suitable resources without the global knowledge of distributed sharing resources. As a consequence, the more nodes the resource discovery service involves, the higher the network overhead incurs. In this paper, we proposed a direction-aware strategy which can alleviate the network traffic among unstructured information systems for distributed resource discovery service. Experimental results have demonstrated that the proposed approach achieves higher success rate at low cost and higher scalability.     

A macroeconomic model for resource allocation in large-scale distributed systems

In this paper we discuss an economic model for resource sharing in large-scale distributed systems. The model captures traditional concepts such as consumer satisfaction and provider revenues and enables us to analyze the effect of different pricing strategies upon measures of performance important for the consumers and the providers. We show that given a particular set of model parameters the satisfaction reaches an optimum; this value represents the perfect balance between the utility and the price paid for resources. Our results confirm that brokers play a very important role and can influence positively the market. We also show that consumer satisfaction does not track the consumer utility; these two important performance measures for consumers behave differently under different pricing strategies. Pricing strategies also affect the revenues obtained by providers, as well as, the ability to satisfy a larger population of users.     

A self-similar super-peer overlay construction scheme for super large-scale P2P applications

Unstructured peer-to-peer (P2P) overlay networks with two-layer hierarchy, comprising an upper layer of super-peers and an underlying layer of ordinary peers, are used to improve the performance of large-scale P2P applications like content distribution and storage. In order to deal with continuous growth of participating peers, a scalable and efficient super-peer overlay topology is essential. However, there is relatively little research conducted on constructing such super-peer overlay topology. In the existed solutions, the number of connections required to be maintained by a super-peer is in direct proportion to the total number of super-peers. For super large-scale P2P applications, i.e. the number of participating peer is over 1,000,000, these solutions are not scalable and impractical. Therefore, in this paper, we propose a scalable hierarchical unstructured P2P system in which a self-similar square network graph (SSNG) is proposed to construct and maintain the super-peer overlay topology adaptively. The SSNG topology is a constant-degree topology in which each node maintains a constant number of neighbor nodes. Moreover, a simple and efficient message forwarding algorithm is presented to ensure each super-peer to receive just one flooding message. The analytical results showed that the proposed SSNG-based overlay is more scalable and efficient than the perfect difference graph (PDG)-based overlay proposed in the literature.     

A taxonomy for resource discovery

Resource discovery systems become more and more important as distributed systems grow and as their pool of resources becomes more variable. As such, an increasing amount of networked systems provide a discovery service. This paper provides a taxonomy for resource discovery systems by defining their design aspects. This allows comparison of the designs of the deployed discovery services and is intended as an aid to system designers when selecting an appropriate mechanism. The surveyed systems are divided into four classes that are separately described. Finally, we identify a hiatus in the design space and point out genuinely distributed resource discovery systems that support dynamic and mobile resources and use attribute-based naming as a main direction for future research in this area.

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一种基于最小代价的网格资源发现模型

引入域和资源路由节点的概念,设置最小代价函数对网格资源进行分层按域划分,形成一种基于最小代价的分层次网格资源发现模型;并为模型设计了相应的资源路由器节点选择算法、资源注册和查询算法。性能分析和模拟实验结果表明,模型具有良好的可扩展性和容错性,且系统代价低;模型能够在屏蔽网格资源异构性的同时很好地满足其动态性、分布性和扩展性的要求,具有较高的资源搜索性能。     

A model for communication between resource discovery and load balancing units in computing environments

Resource overloading causes one of the main challenges in computing environments. In this case, a new resource should be discovered to transfer the extra load. However, this results in drastic performance degradation. Thus, it is of high importance to discover the appropriate resource at first. So far, several resource discovery mechanisms have been introduced to overcome this challenge, a majority of which neglect the fact that this important decision should be made in cooperation with other units existing in a computing environment. One of the units is load balancing. In this paper, we propose a model for communication between resource discovery and load balancing units in a computing environment. Based on the model, resource discovery and load balancing decisions are made cooperatively considering the behavior of running processes and resources capacities. These considerations make decisions more precise. In addition, the model presents the loosest type of coupling between resource discovery and load balancing units, i.e., message coupling. This feature provides a better scalability in size for the model. Comparative results show that the proposed model increases scalability in size by 7 to 15 %, cuts message transmission rate by 15 % and improves hit rate by 51 %.     

基于反馈QoS量化的Web服务发现模型

服务发现是Web服务应用中极其重要的环节.随着Web服务数目的增长,提供相同功能的服务越来越多,现有的基于服务功能描述的服务发现机制越来越难以满足实际需求,于是向Web服务描述内部加入非功能性描述的成为Web服务应用的一个新研究方向.提出了一种基于反馈信息的QoS量化的Web服务发现和选择模型,采用了反馈机制对注册服务QoS进行量化,通过引入概念相近权重,实现了服务质量动态排序,满足了用户的个性化需求,提高服务的查准率.     

Anticipating resource saturation in Federated Grids

In a dynamic and geographically distributed Federated Grid where resources are shared between system participants, there is a lack of mechanisms capable of reallocating already scheduled tasks based on grid infrastructure owners’ current internal needs. In this paper we propose a set of policies for both, users and owners, that aid owners to satisfy internal peak demands and users to achieve the best makespan despite the circumstances. As in our previous work, the main purpose is to do so in the least intrusive way possible to maintain software stack independence of all participants, and to save time and communication bandwidth by anticipating grid resources saturation. These strategies suppose a novel approach for decentralized and non-cooperative workflow scheduling in a federation of heterogeneous grid infrastructures. We evaluate and prove the feasibility of our policies through a set of simulations that reflect the worst case where all resources are saturated. The results show that, in the worst scenario, our scheduling mechanism is beneficial to big infrastructure owners since they can achieve their own internal objectives, as well as to small users since they can reach the best possible completion time.     

基于改进DHT算法的分布式资源发现模型的研究*

为了解决大型分布式系统由集中管理导致的扩展性和鲁棒性差的问题,利用改进的结构化对等网组织分布式计算资源,构造一个SRDM（scalable resource discovery model,可扩展资源发现模型）。SRDM将逻辑空间中的节点分为主机节点和资源节点。主机节点对应分布式环境中的计算节点,用于存储peer关联信息,通过相容性hash映射到逻辑空间上;资源节点对应分布式环境中资源属性信息,其与逻辑空间的映射通过分段hash再合并的方法得到。通过对属性值采用位置保留hash方法,使改进后的DHT算法支持有效的资源节点范围查询和多属性范围查询。最后通过实验证明,基于改进DHT算法的资源发现方法比集中式的方法有更好的扩展性,更适用于大规模分布式系统下的资源发现。     

军事网格中资源发现认证模型研究

通过分析军事网格中资源发现对安全认证的需求和网格中已有的认证模型,提出一种基于身份的资源发现认证模型。该模型以基于身份的公钥密码体制ID-PKC为基础,能够一次完成一个资源请求者和多个资源提供者之间的安全认证,同时完成临时会话密钥的共享。对比分析表明该模型能够提高多信任域环境中多个实体之间双向认证的效率,更符合军事网格环境中资源发现的实际需要。     

A decentralized model for scheduling independent tasks in Federated Grids

In this paper we present a decentralized model for scheduling independent tasks in Federated Grids. This model consists of a set of meta-schedulers on each of the grid infrastructures of the Federated Grid. Each meta-scheduler has to implement a mapping strategy in order to improve two of the most common objective functions of task scheduling problems: makespan and resource performance. We consider four possible algorithms that have to provide a simple, decoupled, and coarse-grained solution that could be deployed in any Grid. The main axis of the algorithms is that they consider the performance of the infrastructures forming the Federated Grid, not only their state.     

基于DSOF的网格资源发现模型研究

为了解决传统资源发现机制不能很好地适应网格资源环境的问题,在有效组建网格资源虚拟组织的基础上,提出了资源虚拟组织中联系结点的选取原则和选取算法,通过该算法从具有相同资源类型的联系结点中选出管理结点.联系结点和管理结点之间采用类似二部图的方式进行连接,构成基于动态自组织覆盖架构的网格资源发现模型.从资源发现能力、资源发现效率和系统可扩展性等方面对该模型进行了综合评价,结果显示该模型适合网格资源的特征,在大规模网格系统中能够有效提高资源发现性能,仿真实验验证了该模型的有效性.     

VEGA Infrastructure for Resource Discovery in Grids

Grids enable users to share and access large collections and various types of re-sources in wide areas, and how to locate resources in such dynamic, heterogeneous and autonomousdistributed environments is a key and challenging issue. In this paper, a three-level decentralizedand dynamic VEGA Infrastructure for Resource Discovery (VIRD) is proposed. In this architec-ture, every Border Grid Resource Name Server (BGRNS) or Grid Resource Name Server (GRNS)has its own local policies, governing information organization, management and searching. Changesin resource information are propagated dynamically among GRNS servers according to a link-state-like algorithm. A client can query its designated GRNS either recursively or iteratively. Optimizing techniques, such as shortcut, are adopted to make the dynamic framework more flexible and effi-cient. A simulator called SimVIRD is developed to verify the proposed architecture and algorithm.q.Experiment results indicate that this architecture could deliver good scalability and performance for grid resource discovery.     

Boolean similarity measures for resource discovery

As the number of Internet servers increases rapidly, it becomes difficult to determine the relevant servers when searching for information. The authors develop a new method to rank Internet servers for Boolean queries. Their method reduces time and space complexity from exponential to polynomial in the number of Boolean terms. They contrast it with other known methods and describe its implementation     

基于匹配算法的服务发现本体模型*

针对服务发现领域存在的匹配问题,提出了基于匹配算法的服务发现本体模型。研究中,以本体技术为基础,分析服务发现模型所包含的主要元素,定义用户本体与服务本体之间最优匹配规划的命题。构建满足该命题的Web服务运行框架。针对运行框架中匹配规划和匹配模式,设计并实现MS算法和MP算法,获取候选匹配集及匹配规划的相关度。与现有服务发现方法相比,提出的服务发现本体模型具有较高的查全率与查准率,能够获得更多贴近用户服务请求的Web服务,具有较好的理论价值和应用前景。     

An optimization-based adaptive resource management framework for economic Grids: A switching mechanism

The application of Grid computing has been broadening day by day. An increasing number of users has led to the requirement of a job scheduling process, which can benefit them through optimizing their utility functions. On the other hand, resource providers are exploring strategies suitable for economically efficient resource allocation so that they can maximize their profit through satisfying more users. In such a scenario, economic-based resource management strategies (economic models) have been found to be compelling to satisfy both communities. However, existing research has identified that different economic models are suitable for different scenarios in Grid computing. The Grid application and resource models are typically very dynamic, making it challenging for a particular model for delivering stable performance all the time. In this work, our focus is to develop an adaptive resource management architecture capable of dealing with multiple models based on the models’ domains of strengths (DOS). Our preliminary results show promising outcomes if we consider multiple models rather than relying on a single model throughout the life cycle of a Grid.     

A distributed resource discovery algorithm for P2P grids

Centralized or hierarchical administration of the classical grid resource discovery approaches is unable to efficiently manage the highly dynamic large-scale grid environments. Peer-to-peer (P2P) overlay represents a dynamic, scalable, and decentralized prospect of the grids. Structured P2P methods do not fully support the multi-attribute range queries and unstructured P2P resource discovery methods suffer from the network-wide broadcast storm problem. In this paper, a decentralized learning automata-based resource discovery algorithm is proposed for large-scale P2P grids. The proposed method supports the multi-attribute range queries and forwards the resource queries through the shortest path ending at the grid peers more likely having the requested resource. Several simulation experiments are conducted to show the efficiency of the proposed algorithm. Numerical results reveal the superiority of the proposed model over the other methods in terms of the average hop count, average hit ratio, and control message overhead.     

Composing Web services for large-scale tasks

By making it easy to explore combinations of multiple Web services, Eurasia helps users tackle large-scale information-management tasks and adapt and reuse the steps involved.     

Computational and data Grids in large-scale science and engineering

As the practice of science moves beyond the single investigator due to the complexity of the problems that now dominate science, large collaborative and multi-institutional teams are needed to address these problems. In order to support this shift in science, the computing and data-handling infrastructure that is essential to most of modern science must also change in order to support this increased complexity. This is the goal of computing and data Grids: software infrastructure that facilitates solving large-scale problems by providing the mechanisms to access, aggregate, and manage the computer network-based infrastructure of science. This infrastructure includes computing systems, data archive systems, scientific instruments, and computer-mediated human collaborations. This paper examines several large-scale science problems, their requirements for computing and data Grid infrastructure, and the current approaches to providing the necessary functionality.